Background: The Uniform Data Set, Version 3 Neuropsychological Battery (UDSNB3.0), from the database of the University of Washington's National Alzheimer's Coordinating Center (NACC), is widely used to characterize cognitive performance in clinical and research settings; however, norms for underrepresented community-based samples are scarce. Objective: We compared UDSNB 3.0 test scores between the Einstein Aging Study (EAS), composed of racially/ethnically diverse, community-dwelling older adults aged≥70 and the NACC, and report normative data from the EAS. Methods: Analyses included 225 cognitively normal EAS participants and comparable data from 5,031 NACC database participants. Linear regression models compared performance between the samples, adjusting for demographics (sex, age, education, race/ethnicity), depressive symptoms, and whether English was the first language. Linear regression models to examine demographic factors including age, sex, education and race/ethnicity as predictors for the neuropsychological tests were applied in EAS and NACC separately and were used to create a demographically adjusted z-score calculator. Results: Cognitive performance across all domains was worse in the EAS than in the NACC, adjusting for age, sex, education, race/ethnicity, and depression, and the differences remained in visuo-construction, visuospatial memory, confrontation naming, visual attention/processing speed, and executive functioning after further adjusting for whether English was the first language. In both samples, non-Hispanic Whites outperformed non-Hispanic Blacks and more education was associated with better cognitive performance. Conclusion: Differences observed in demographic, clinical, and cognitive characteristics between the community-based EAS sample and the nationwide NACC sample suggest that separate normative data that more accurately reflect non-clinic, community-based populations should be established.
All Science Journal Classification (ASJC) codes
- Clinical Psychology
- Geriatrics and Gerontology
- Psychiatry and Mental health